Racial differences in length of stay and readmission for asthma in the all of us research program

Background This study addresses the limited research on racial disparities in asthma hospitalization outcomes, specifically length of stay (LOS) and readmission, across the U.S. Methods We analyzed in-patient and emergency department visits from the All of Us Research Program, identifying various risk factors (demographic, comorbid, temporal, and place-based) associated with asthma LOS and 30-day readmission using Bayesian mixed-effects models. Results Of 17,233 patients (48.0% White, 30.7% Black, 19.7% Hispanic/Latino, 1.3% Asian, and 0.3% Middle Eastern and North African) with 82,188 asthma visits, Black participants had 20% shorter LOS and 12% higher odds of readmission, compared to White participants in multivariate analyses. Public-insured patients had 14% longer LOS and 39% higher readmission odds than commercially insured patients. Weekend admissions resulted in a 12% shorter LOS but 10% higher readmission odds. Asthmatics with chronic diseases had a longer LOS (range: 6–39%) and higher readmission odds (range: 9–32%) except for those with allergic rhinitis, who had a 23% shorter LOS. Conclusions A comprehensive understanding of the factors influencing asthma hospitalization, in conjunction with diverse datasets and clinical-community partnerships, can help physicians and policymakers to systematically address racial disparities, healthcare utilization and equitable outcomes in asthma care. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-023-04826-9.

times more likely to be hospitalized and seven times more likely to die from asthma compared to European Americans (EAs) [6].In addition, asthma is often accompanied by allergic comorbidities, such as allergic rhinitis, atopic dermatitis, and food allergy in children, as well as non-allergic disorders such as obesity, gastro-esophageal reflux, and mental health disorders [7].How these intrinsic and extrinsic risk factors differentially impact asthma LOS and readmission among racial groups remain poorly understood.
Previous studies have investigated risk factors and racial differences associated with LOS and readmission in asthma.Chen et al. conducted a systematic review of single-center studies examining asthma LOS and clinical pathways [8].In multi-site studies, the Hospital Episode Statistics (HES) cohort was one of the first to investigate risk factors associated to asthma LOS in London hospitals [9].Similarly, Shanley et al. identified factors associated with LOS for pediatric asthma hospital admissions in the Pediatric Health Information System (PHIS) database [10].More recently, Kaiser et al., reported the difficult of asthma LOS improvement using clinical pathways in a diverse, national group of hospitals in the U.S [11].Despite the well-documented evidence of race differences and risk factors in asthma hospitalization, current studies have been primarily focused on European Americans in the U.S. Additionally, current studies are limited to small sample size and fewer risk factors due to lack of standardized data across different institutions.
These limitations may affect the unique position of physicians and policymakers on understanding the factors influencing asthma LOS and readmission to improve healthcare cost and asthma care.Therefore, we sought to characterize clinical and socioeconomic factors associated to asthma LOS in the U.S. adults using the All of Us Research Program data (hereafter termed as All of Us).All of Us, funded by the National Institutes of Health (NIH), is a cohort study aiming to enroll one million U.S. adults, particularly from underrepresented groups.Currently, the program has enrolled 413,457 participants from different races, ethnicities, age groups, and regions of the country, and collects data through survey questionnaires, electronic health records, physical measurements, linked exposures, and biospecimens (https:// allof us.nih.gov/).By using a large and diverse database, this study aims to aid physicians and policymakers to better understand race differences in asthma LOS and readmission, to inform future policy initiatives aimed at improving healthcare costs and asthma care at the national level [12].

Study design and population
Participants with asthma from over 340 recruitment sites nationwide were identified in the All of Us database version 7, using the International Classification of Diseases, 9th and 10th revisions codes for asthma diagnoses (ICD-9-496 and ICD-10-J45).For the processing of hospital admissions, if two recorded asthma visits happened on the same day, they were considered to have the same first visit date.We only included inpatient and ED visits, and excluded those visits with missing admission discharge/phenotype information and unreliable length of stay in hospital (Ten standard deviations away from the mean).Figure 1 shows the workflow for the exclusion criteria.Additional information about the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist is provided in the Additional file 1 (Table S1).

Asthma length of stay and readmission
Length of stay per admission (LOS) is a surrogate measure of hospital utilization and refers to the number of days spent in the hospital (i.e., the difference in days between the first date of admission and the last date of discharge) [12,13].For our dataset, asthma LOS per admission can be zero for discharge in the same day, or larger if the patient required additional time to discharge.Furthermore, we considered the readmission of asthma patients within 30 days, inclusive of the length of stay (LOS), as a secondary outcome measure.This measure is defined as the difference in start dates between two consecutive events for a single patient, provided there is no overlap between the events [14].

Covariates
The following individual-level risk factors (demographics, comorbidities, temporal and place-based risk) for asthma were chosen based on their association with the condition in prior research and their availability in the EHR database [10,[15][16][17][18].

Demographic
The EHR data collected encompassed demographic variables such as sex at birth (classified as female or male), age at visit, and self-reported race/ethnicity (classified as Black, White, Hispanic/Latino [HL], or Middle Eastern and North African [MENA].Survey questionnaires were used to infer individual-level health insurance information (classified as private or public).

Comorbidities
The current list of comorbidities was selected according to an evidence synthesis process of relevant references [10,16,17] Using the appropriate ICD-9 and ICD-10 codes, our list of clinical diagnoses included atopic dermatitis (AD), allergic rhinitis (AR), cancer, coronary heart disease (CHD), chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), depression, diabetes mellitus, eosinophilic esophagitis (EoE), food allergy (FA), gastro-esophageal reflux disease (GERD), hypertension (HTA), obesity, psoriasis, and sleep apnea.We developed a patient-level dummy variable to track clinical history of comorbidities prior to the subject's visit ("Yes" if the patient has been previously diagnosed with the comorbid-specific disease and "No" otherwise).For instance, in the asthma encounter, if a specific patient had a previous diagnosis of any comorbidity, the comorbid-specific dummy variable for their asthma encounter was set to "Yes" accordingly.Full list of ICD-9 and ICD-10 codes is included in the Additional file 1 (Table S2).

Temporal and place based
Temporal data on date of admission refers to weekday (Monday to Friday) or weekend (Saturday and Sunday).
We categorized visit data by meteorological seasons: winter (December, January, and February), spring (March, April, and May), summer (June, July, and August), and fall (September, October, and November).All of Us database includes area-level estimation of material deprivation linked to the latest known patient zip-code.Details about this composite socioeconomic index can be found elsewhere [19].

Statistical analysis
Descriptive statistics were calculated for continuous (means with SD) and categorical (frequencies with percentages) variables and stratified by race/ethnicity.Associations between race/ethnicity and each variable were tested using chi-square ( X 2 ) or t tests, as appropriate.To assess the association of risk factors with asthma LOS and readmission, we constructed a directed acyclic graph (DAG) with backdoor criterion to aid covariate adjustment following causal inference principles and our previous work (Fig. 2) [20][21][22].We employed Bayesian mixed-effects negative binomial regression models to estimate the LOS in days.Additionally, we utilized logistic regression models to predict readmissions within a 30-day period.Both of these models were used in their univariate and multivariate forms and continuous variables (age and deprivation) were centered for numerical stability.Both, univariate and multivariate models account for subject-specific and multi-site variance including random intercepts for patients with recurrent asthma-related visits, and for EHR sites.Multivariate models included only significant risk factors, and the interaction effect between age and sex because of their significant association with asthma and included comorbidities [15].Finally, we report Bayesian credible intervals (CI) using a 95% probability.The Methods section in this article's Online Repository provides additional details about model formulation, univariate expected LOS and readmission, models formulation, posterior predictive distribution diagnostics, and priors used (Additional file 1: Figures S1-S2).Data extraction was performed on Python using bigquery, analyses were conducted using the R environment, ggplot for graphics, rstanarm for multivariate modelling [23].

Model assessment
We compared Bayesian multivariate expected length of stay (LOS) and readmission models (null and adjusted set of covariates) using the expected log predictive density (ELPD) [24].ELPD quantifies the theoretical expected log pointwise predictive density for new observations, where a higher ELPD score indicates better model fit.Because our models account for recurrent asthma-related visits, we adopted a leave-one-group-out (LOGO) scheme in the cross-validation computation (one patient, multiple asthma events) (Additional file 1).

Risk factors for asthma length of stay (LOS)
Table 2 summarizes the results for the association between asthma LOS and the assessed independent variables.Season of admission, deprivation, previous AD, EoE, FA, and Psoriasis were not associated to asthma LOS in the univariate analysis (Table S3).depression, GERD, and HTA, were associated with higher odds of asthma readmission (range: 9-32%).

Validation
Table S4 shows validation performance in expected length of stay (LOS) and readmission.Adjusted models had better ELPD in both outcomes (LOS and readmission) compared to the null (ELPD diff > 4).

Discussion
Here we report differences in demographic, comorbidities, and temporal and place-based risk factors for asthma LOS and readmission in a large and diverse All of Us dataset.Compared to White, Black and Hispanic/ Latino (HL) experienced shorter than average asthma LOS, and higher proportions of readmission.Our multivariate analyses suggest that race differences in asthma LOS and readmission were mainly driven by health insurance, and day of admission.Patients with clinical history of several chronic diseases were associated with longer LOS and increased odds of readmission, whereas patients with previous AR were associated with shorter LOS.
In our multivariate analyses, we found differences in LOS and probability of readmission among different racial groups.Specifically, Black and HL adults had shorter LOS, but only Black individuals showed increased odds of asthma readmission compared to the White individuals.This aligns with previous studies from our group and others, which have frequently reported worse asthma outcomes among minority populations [21,22,25].We hypothesize that certain factors indirectly related to race, such as health insurance status, and day of admission, could explain the observed negative correlation between shorter LOS due  For example, the so-called "weekend effect"-the phenomenon of patients admitted over the weekend being discharged earlier-influenced by various patient and health service-related factors (e.g., quality of care, medication errors, bed occupancy rates, staff levels, disease management education) could result in posterior readmissions [10,26,27].Unfortunately, these factors are more prevalent among low-income groups who often seek care on weekends due to limited options (e.g., accessibility barriers).Links between age and related asthma outcomes are consistent with the literature including our group [15,22].Such links also extend to other comorbidities evaluated in our manuscript [10,16,17].Further research is needed to explore these relationships specifically in asthma, and develop strategies (e.g., telemedicine, internal processes and EHR, attending schedules and nighttime in clinical settings) [28,29] to improve asthma outcomes among these vulnerable populations.
Our findings revealed that having a clinical history of comorbidities is associated with asthma LOS and readmission differences.We observed a group consisting of chronic diseases (CHD, CKD, COPD, depression, GERD, HTA, obesity, and sleep apnea) that may prolong asthma LOS or increase odds of asthma readmission, and a second group consisting of allergic diseases such as AR that may shorten it.The former group has been well-documented with supported evidence as major comorbid diseases with asthma.For example, Black and Latino asthmatics are disproportionally affected by asthma related comorbidities including obesity or chronic diseases [17,30].Our study could help physicians to develop targeted interventions such as a clinical risk profile in daily practice leading to earlier diagnosis and treatment of comorbidities with subsequent improvement in outcome and reduce the asthma burden, especially in minorities.The latter group could be explained by the fact that asthma events in individuals with a clinical history of Type 2 immune disorders may be more frequent as previously reported by others authors, but not necessarily more severe [31][32][33].Previous research has shown that treatment for AR (e.g., inhaled/oral steroids or cromolyn) can reduce the risk of hospitalization and ER visits for asthma patients [34].We hypothesize asthma patients with a co-occurrence of AR are more frequently admitted in hospital for asthma but may recover more quickly once admitted due to their beneficial treatment impacts, exhibiting a shorter hospital stay.However, effects from diseasespecific comorbidities on asthma are remain poorly understood and require further investigation to achieve a reduction in asthma healthcare utilization.
There are a number of limitations associated with this study.First, although this study was conducted in a large and diverse admission/discharge standardized dataset across more than 340 locations in the U.S., the findings may not be generalizable to non-All of Us member institutions.Second, race/ethnicity was determined based on self-reported information collected and stored in the electronic health record (EHR).We recognize that selfreported race is a social construct and does not reflect genetic ancestry.As such, it should be considered solely as a sociopolitical construct, rather than a marker of biological differences [35].We also recognize that the sample size for Asian and Middle Eastern and North African are relatively small.Future research could include more samples from these population, which is becoming increasingly available in the All of Us program [36].Third, we had limited individual-level data available within the EHR that may affect an accurate estimation of LOS.Future research might include potential clinical pathway information about the hospital stay (e.g., physiologic readiness for discharge [PRD], bronchodilator/ corticosteroid usage, and oxygen saturation) that were not available in our study [37,38].Fourth, due to the lack of precise patient-level environmental data, our estimate of place-based deprivation, based only on the last known area-level location.This may not accurately capture patient-level socioeconomic context specially those who change their physical address.Also, using placebased deprivation as a proxy of patient-level socioeconomic context might lead to bias (ecological fallacy) [39].Finally, this study relied on routinely collected ICD-9/ ICD-10 billing codes associated to asthma.Other unobserved clinical and health-related factors that occur during the admission discharge window could influence the variation in length of stay.

Conclusions
To our knowledge, this is the first study to characterize demographic, clinical history of comorbidities, temporal and socioeconomic risk factors associated to asthma LOS and asthma readmission in a large and diverse cohort as All of Us.We reported more than 82,000 recurrent in-patient and ED hospital admissions from about 17,000 U.S. adults including underrepresented groups to examine epidemiological differences in asthma LOS and asthma readmission from a broad range of variables with an explicit causal thinking.Some public health policies to address racial disparities could be derived from this study.First, promote the "meaningful use" of the electronic health record in the care of asthmatic patients.For example, exploring integration and the optimal use of the electronic health record to tailor clinical pathways and health interventions could help improve access and asthma care for groups at risk [40].Second, clinical-community partnerships can collaborate to systematically address modifiable place-based risk factors improving health and well-being.Strategies to link clinical partners and communities aimed to reduce health disparities have been successfully documented and can be adapted to adult asthma population (41).We identified how asthma LOS and asthma readmission differs by race, health insurance, clinical history of comorbidities, and day of admission.The identification of these factors in a multisite and diverse cohort could provide potential areas of future research aimed to reduce healthcare utilization and achieve equitable outcomes in asthma-related morbidity across the U.S.

Fig. 1
Fig. 1 Flowchart of in-patient and asthma ED visit inclusion and exclusion criteria.The final study population includes a total of 17,233 children with 82,188 asthma ED visit

Fig. 2
Fig. 2 Directed acyclic graph (DAG) for the independent variable (race, clinical history of comorbidities, temporal, place-based deprivation) and the dependent variable (Length of stay [LOS], and readmission).Causal pathways, covariates, confounders, and unobserved (latent) variables are presented

Table 1
In-patient and ED asthma visits by self-reported race including demographic, clinical history of comorbidities, temporal and place-based risk factors

Table 2
compared to commercially-insured counterparts.Being admitted during the weekend had 10% higher odds of readmission (OR = 1.10,CI = 1.05 to 1.15) compared to a weekday admission.Examining previously diagnosed comorbidities in readmission, CHD, CKD, COPD,

Table 1 (
continued) LOS Length of stay, SD Standard deviation, AD Atopic dermatitis, AR Allergic rhinitis, CHD Coronary heart disease, CKD Chronic kidney disease, COPD Chronic obstructive pulmonary disease, EoE Eosinophilic esophagitis, FA Food allergy, GERD Gastro-esophageal reflux disease, HL Hispanic/Latino, HTA Hypertension, MENA Middle Eastern and North Africa

Table 2
Bayesian multivariate expected length of stay (LOS) and readmission within 30 days showing median and 95% credible intervals for associations between asthma hospitalizations and significant risk factors (demographics, individual-comorbidities, temporal and place-based)

Table 2
(continued) OR Odds ratio, CI Credible interval, AR Allergic rhinitis, CHD Coronary heart disease, CKD Chronic kidney disease, COPD Chronic obstructive pulmonary disease, FA Food allergy, GERD Gastro-esophageal reflux disease, HL Hispanic/Latino, HTA Hypertension, MENA Middle Eastern and North Africa to an early discharge and the increased odds of readmission among black patients.Such factors have been linked to decreased quality of care in other studies.